import os
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
%matplotlib inline

Importing data set

data = pd.read_excel('netflix_titles.xlsx')
data.head(10)

	show_id	type	title	director	cast	country	date_added	release_year	rating	duration	listed_in	description
0	s1	Movie	Dick Johnson Is Dead	Kirsten Johnson	NaN	United States	2021-09-25 00:00:00	2020	PG-13	90 min	Documentaries	As her father nears the end of his life, filmm...
1	s2	TV Show	Blood & Water	NaN	Ama Qamata, Khosi Ngema, Gail Mabalane, Thaban...	South Africa	2021-09-24 00:00:00	2021	TV-MA	2 Seasons	International TV Shows, TV Dramas, TV Mysteries	After crossing paths at a party, a Cape Town t...
2	s3	TV Show	Ganglands	Julien Leclercq	Sami Bouajila, Tracy Gotoas, Samuel Jouy, Nabi...	NaN	2021-09-24 00:00:00	2021	TV-MA	1 Season	Crime TV Shows, International TV Shows, TV Act...	To protect his family from a powerful drug lor...
3	s4	TV Show	Jailbirds New Orleans	NaN	NaN	NaN	2021-09-24 00:00:00	2021	TV-MA	1 Season	Docuseries, Reality TV	Feuds, flirtations and toilet talk go down amo...
4	s5	TV Show	Kota Factory	NaN	Mayur More, Jitendra Kumar, Ranjan Raj, Alam K...	India	2021-09-24 00:00:00	2021	TV-MA	2 Seasons	International TV Shows, Romantic TV Shows, TV ...	In a city of coaching centers known to train I...
5	s6	TV Show	Midnight Mass	Mike Flanagan	Kate Siegel, Zach Gilford, Hamish Linklater, H...	NaN	2021-09-24 00:00:00	2021	TV-MA	1 Season	TV Dramas, TV Horror, TV Mysteries	The arrival of a charismatic young priest brin...
6	s7	Movie	My Little Pony: A New Generation	Robert Cullen, José Luis Ucha	Vanessa Hudgens, Kimiko Glenn, James Marsden, ...	NaN	2021-09-24 00:00:00	2021	PG	91 min	Children & Family Movies	Equestria's divided. But a bright-eyed hero be...
7	s8	Movie	Sankofa	Haile Gerima	Kofi Ghanaba, Oyafunmike Ogunlano, Alexandra D...	United States, Ghana, Burkina Faso, United Kin...	2021-09-24 00:00:00	1993	TV-MA	125 min	Dramas, Independent Movies, International Movies	On a photo shoot in Ghana, an American model s...
8	s9	TV Show	The Great British Baking Show	Andy Devonshire	Mel Giedroyc, Sue Perkins, Mary Berry, Paul Ho...	United Kingdom	2021-09-24 00:00:00	2021	TV-14	9 Seasons	British TV Shows, Reality TV	A talented batch of amateur bakers face off in...
9	s10	Movie	The Starling	Theodore Melfi	Melissa McCarthy, Chris O'Dowd, Kevin Kline, T...	United States	2021-09-24 00:00:00	2021	PG-13	104 min	Comedies, Dramas	A woman adjusting to life after a loss contend...

print(f"Dataset contains: {data.shape[0]} records")
print(f"It contains {data.shape[1]} features, namely: {', '.join(data.columns)}")

Dataset contains: 8807 records
It contains 12 features, namely: show_id, type, title, director, cast, country, date_added, release_year, rating, duration, listed_in, description

Data cleaning

Checking data types & non null values

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8807 entries, 0 to 8806
Data columns (total 12 columns):
 #   Column        Non-Null Count  Dtype 
---  ------        --------------  ----- 
 0   show_id       8807 non-null   object
 1   type          8807 non-null   object
 2   title         8807 non-null   object
 3   director      6173 non-null   object
 4   cast          7982 non-null   object
 5   country       7976 non-null   object
 6   date_added    8797 non-null   object
 7   release_year  8807 non-null   int64 
 8   rating        8803 non-null   object
 9   duration      8804 non-null   object
 10  listed_in     8807 non-null   object
 11  description   8807 non-null   object
dtypes: int64(1), object(11)
memory usage: 825.8+ KB

Changing 'date_added' to datetime type

data['date_added'] = pd.to_datetime(data['date_added'], errors = 'coerce')
data.head()

	show_id	type	title	director	cast	country	date_added	release_year	rating	duration	listed_in	description
0	s1	Movie	Dick Johnson Is Dead	Kirsten Johnson	NaN	United States	2021-09-25	2020	PG-13	90 min	Documentaries	As her father nears the end of his life, filmm...
1	s2	TV Show	Blood & Water	NaN	Ama Qamata, Khosi Ngema, Gail Mabalane, Thaban...	South Africa	2021-09-24	2021	TV-MA	2 Seasons	International TV Shows, TV Dramas, TV Mysteries	After crossing paths at a party, a Cape Town t...
2	s3	TV Show	Ganglands	Julien Leclercq	Sami Bouajila, Tracy Gotoas, Samuel Jouy, Nabi...	NaN	2021-09-24	2021	TV-MA	1 Season	Crime TV Shows, International TV Shows, TV Act...	To protect his family from a powerful drug lor...
3	s4	TV Show	Jailbirds New Orleans	NaN	NaN	NaN	2021-09-24	2021	TV-MA	1 Season	Docuseries, Reality TV	Feuds, flirtations and toilet talk go down amo...
4	s5	TV Show	Kota Factory	NaN	Mayur More, Jitendra Kumar, Ranjan Raj, Alam K...	India	2021-09-24	2021	TV-MA	2 Seasons	International TV Shows, Romantic TV Shows, TV ...	In a city of coaching centers known to train I...

Check number of missing values

null_count = data.isnull().sum()
print(f'Number of missing values: {null_count}')

Number of missing values: show_id            0
type               0
title              0
director        2634
cast             825
country          831
date_added        10
release_year       0
rating             4
duration           3
listed_in          0
description        0
dtype: int64

Since the number of missing values is not extreme, I will leave them as such for now and handle them later as needed

Rename 'listed_in' to 'category'

data = data.rename(columns={'listed_in': 'category'})

Now we can strip any leading/trailing spaces in any string values

data = data.map(lambda x : x.strip() if isinstance(x, str) else x)

data.head(10)

	show_id	type	title	director	cast	country	date_added	release_year	rating	duration	category	description
0	s1	Movie	Dick Johnson Is Dead	Kirsten Johnson	NaN	United States	2021-09-25	2020	PG-13	90 min	Documentaries	As her father nears the end of his life, filmm...
1	s2	TV Show	Blood & Water	NaN	Ama Qamata, Khosi Ngema, Gail Mabalane, Thaban...	South Africa	2021-09-24	2021	TV-MA	2 Seasons	International TV Shows, TV Dramas, TV Mysteries	After crossing paths at a party, a Cape Town t...
2	s3	TV Show	Ganglands	Julien Leclercq	Sami Bouajila, Tracy Gotoas, Samuel Jouy, Nabi...	NaN	2021-09-24	2021	TV-MA	1 Season	Crime TV Shows, International TV Shows, TV Act...	To protect his family from a powerful drug lor...
3	s4	TV Show	Jailbirds New Orleans	NaN	NaN	NaN	2021-09-24	2021	TV-MA	1 Season	Docuseries, Reality TV	Feuds, flirtations and toilet talk go down amo...
4	s5	TV Show	Kota Factory	NaN	Mayur More, Jitendra Kumar, Ranjan Raj, Alam K...	India	2021-09-24	2021	TV-MA	2 Seasons	International TV Shows, Romantic TV Shows, TV ...	In a city of coaching centers known to train I...
5	s6	TV Show	Midnight Mass	Mike Flanagan	Kate Siegel, Zach Gilford, Hamish Linklater, H...	NaN	2021-09-24	2021	TV-MA	1 Season	TV Dramas, TV Horror, TV Mysteries	The arrival of a charismatic young priest brin...
6	s7	Movie	My Little Pony: A New Generation	Robert Cullen, José Luis Ucha	Vanessa Hudgens, Kimiko Glenn, James Marsden, ...	NaN	2021-09-24	2021	PG	91 min	Children & Family Movies	Equestria's divided. But a bright-eyed hero be...
7	s8	Movie	Sankofa	Haile Gerima	Kofi Ghanaba, Oyafunmike Ogunlano, Alexandra D...	United States, Ghana, Burkina Faso, United Kin...	2021-09-24	1993	TV-MA	125 min	Dramas, Independent Movies, International Movies	On a photo shoot in Ghana, an American model s...
8	s9	TV Show	The Great British Baking Show	Andy Devonshire	Mel Giedroyc, Sue Perkins, Mary Berry, Paul Ho...	United Kingdom	2021-09-24	2021	TV-14	9 Seasons	British TV Shows, Reality TV	A talented batch of amateur bakers face off in...
9	s10	Movie	The Starling	Theodore Melfi	Melissa McCarthy, Chris O'Dowd, Kevin Kline, T...	United States	2021-09-24	2021	PG-13	104 min	Comedies, Dramas	A woman adjusting to life after a loss contend...

Director & cast has multiple entries so we need to seperate them before plotting

director_count = data['director'].str.split(', ').explode().value_counts().head(10)
cast_count = data['cast'].str.split(', ').explode().value_counts().head(10)
category_count = data['category'].str.split(', ').explode().value_counts()

Data is ready for plotting

data['type'].value_counts()

type
Movie      6131
TV Show    2676
Name: count, dtype: int64

Visualizations

# Overall media type distribution

plt.figure(figsize=(8,6))

ax = sns.countplot(
    x='type',
    data = data, 
    alpha = 0.5, 
    hue='type')

plt.title("Distribution of Media Type", fontsize=16, fontweight='bold')

for container in ax.containers:
    ax.bar_label(container, label_type='edge', padding=2, fontsize=10)
    
plt.tight_layout()
plt.show()

As seen in plot above, this dataset has significantly more movies than TV shows

# Ratings Distribution on the platform

plt.figure(figsize=(10, 5))

top_ratings = data['rating'].value_counts().head(10).index

ax = sns.countplot(
    y='rating',
    data=data[data['rating'].isin(top_ratings)],
    order=top_ratings,
    hue='rating'
)

plt.title("Distribution of Ratings", fontsize=16, fontweight='bold')
for container in ax.containers:
    ax.bar_label(container, label_type='edge', padding=2, fontsize=10)

plt.show()

As seen above, majority of content in the dataset is TV-MA and TV-14.

# Top countries producing movies/TV shows

plt.figure(figsize=(10, 6))

ax = sns.countplot(
    y='country', 
    data=data, 
    order=data['country'].value_counts().index[:10])

plt.title("Top 10 Countries by title count", fontsize=16, fontweight='bold')

for container in ax.containers:
    ax.bar_label(container)

plt.show()

The figure above shows that United States leads with 2818 titles with India in distant second with 972 titles

# Type distribution in top producing countries

plt.figure(figsize=(10, 6))
index_labels = list(data['country'].value_counts().head(10).index)

type_by_countries=data[data['country'].isin(index_labels)]

ax=sns.countplot(x='country',data=type_by_countries, palette='magma',hue='type', order=index_labels)
for container in ax.containers:
    ax.bar_label(container)

plt.xticks(rotation=60)
plt.title("Type Distribution in Top 10 Countries", fontsize=16, fontweight='bold')
plt.show()

The plot above shows more movies being produced comapred to TV shows in majority of the top 10 countries

# Release year distribution on the platform

release_year_plot=(data['release_year']
                    .value_counts()
                    .nlargest(20)
                    .reset_index(name= 'count')
                    .rename(columns={'index': 'release_year'})
)
fig = px.line(release_year_plot.sort_values('release_year'), 
              y='count',
              x='release_year', 
              markers=True,
              title='Distribution of media produced by year',
              labels={'release_year': 'Year', 'count': 'Number of Titles'})

fig.show()

The plot above shows the year in which the media was produced, that's available on Netflix currently. The trend goes downward after 2020 significantly due to not enough new media produced because of covid-19 pandemic. This also indicates that with the advent of streaming services like Netflix, significantly more media is being produced now.

# Category distribution of media on the platfornm

# Movies
movie_categories = (
    data[data['type'] == 'Movie']['category']
    .str.split(', ')
    .explode()
    .value_counts()
    .head(8)
)

# TV shows
tv_categories = (
    data[data['type'] == 'TV Show']['category']
    .str.split(', ')
    .explode()
    .value_counts()
    .head(8)
)

fig, ax = plt.subplots(1, 2, figsize=(12, 8))

# Pie chart for Movies
ax[0].pie(movie_categories.values, labels=movie_categories.index, autopct='%1.1f%%')
ax[0].set_title('Top 10 Categories in Movies', fontsize= 16, fontweight= 'bold')

# Pie chart for TV Shows
ax[1].pie(tv_categories.values, labels=tv_categories.index, autopct='%1.1f%%')
ax[1].set_title('Top 10 Categories in TV Shows', fontsize= 16, fontweight= 'bold')

plt.tight_layout()
plt.show()

The biggest category being International movies and TV drama explains why Netflix has invested so much in foreign made media. They've had lot of success with these investments.

Now we finally drop all missing values from below columns

#data = data.dropna(subset=['director', 'cast', 'country'])

# Top 10 directors & cast members overall

plt.figure(figsize=(10,6))
ax = sns.barplot(
    x=director_count.values, 
    y=director_count.index, 
    alpha=0.8, 
    palette='magma')

plt.title('Top 10 Directors', fontsize=16, fontweight= 'bold')

for container in ax.containers:
    ax.bar_label(container, label_type='edge', padding=2)
    
plt.figure(figsize=(10,6))
ax = sns.barplot(
    x=cast_count.values, 
    y=cast_count.index, 
    alpha=0.8, 
    palette='viridis')

plt.title('Top 10 Cast Members', fontsize=16, fontweight= 'bold')

for container in ax.containers:
    ax.bar_label(container, label_type='edge', padding=2)

plt.tight_layout()
plt.show()

/var/folders/l5/7wkdfd8s1ds9m7fptfxlcv_m0000gn/T/ipykernel_20872/3966626257.py:4: FutureWarning:



Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.


/var/folders/l5/7wkdfd8s1ds9m7fptfxlcv_m0000gn/T/ipykernel_20872/3966626257.py:16: FutureWarning:



Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.

The figures above show a varied roster of cast & directors across nations showing the wide variety of talent on the platform.

# Distribution of movie releases per genre yearly

data['category'] = data['category'].str.split(', ')
data = data.explode('category')

# Movies
movies = data.loc[data['type'] == 'Movie']

# Top 5 movie genres
top_movie_genres = (
                movies['category']
                .value_counts()
                .nlargest(5).
                index
)
movies = movies[movies['category'].isin(top_movie_genres)]

movies_grouped = (
            movies.groupby(['release_year', 'category'])
            .size()
            .reset_index(name='count')
)

fig_movies = px.line(
    movies_grouped,
    x='release_year',
    y='count',
    color='category',
    markers=True,
    title='Top 5 Movie Genres Over Time',
    labels={'release_year': 'Year', 'count': 'Number of Releases', 'category': 'Genre'}
)

fig_movies.show()

# TV Shows
tv_shows = data.loc[data['type'] == 'TV Show']

# Top 5 TV show genres
top_tv_genres = (
            tv_shows['category']
            .value_counts()
            .nlargest(5)
            .index
)
tv_shows = tv_shows[tv_shows['category'].isin(top_tv_genres)]

tv_grouped = (
        tv_shows.groupby(['release_year', 'category'])
        .size()
        .reset_index(name='count')
)

fig_tv = px.line(
    tv_grouped,
    x='release_year',
    y='count',
    color='category',
    markers=True,
    title='Top 5 TV Show Genres Over Time',
    labels={'release_year': 'Year', 'count': 'Number of Releases', 'category': 'Genre'}
)

fig_tv.show()

The figure above shows that the push for international movies & TV shows has been there for quite some years now on Netflix and is not necessarily a new phenomenon as hinted by genre distribution pie chart earlier. Crime TV shows initially rose around 2015 and peaked in 2019, have now seen a downward trend, which coincides with other genres likely an outcome caused by global pandemic. Whereas action & adventure movies were outpaced by Documentaries around 2015.

This concludes the exploratory data analysis on the Netflix dataset as available on kaggle, dated Jul 03, 2025. The .ipynb file includes downloading the dataset locally, then reading the file, doing analysis & finally some interesting visualizations to paint the story behind the thousands of rows and column data.

I hope this sample demonstrates some of the my python data analysis knowledge and serves as an example of my skills.